LINR Bridge: Vector Graphic Animation via Neural Implicits and Video Diffusion Priors

• URL: http://arxiv.org/abs/2509.07484v1
• Date: Tue, 09 Sep 2025 08:04:36 GMT
• Title: LINR Bridge: Vector Graphic Animation via Neural Implicits and Video Diffusion Priors
• Abstract summary: We propose a novel method that integrates implicit neural representations with text-to-video diffusion models for vector graphic animation.<n>Our approach employs layered implicit neural representations to reconstruct vector graphics, preserving their inherent properties such as infinite resolution and precise color and shape constraints.<n>The neural representations are then optimized using video score distillation sampling, which leverages motion priors from pretrained text-to-video diffusion models.
• Score: 8.74744557581647
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vector graphics, known for their scalability and user-friendliness, provide a unique approach to visual content compared to traditional pixel-based images. Animation of these graphics, driven by the motion of their elements, offers enhanced comprehensibility and controllability but often requires substantial manual effort. To automate this process, we propose a novel method that integrates implicit neural representations with text-to-video diffusion models for vector graphic animation. Our approach employs layered implicit neural representations to reconstruct vector graphics, preserving their inherent properties such as infinite resolution and precise color and shape constraints, which effectively bridges the large domain gap between vector graphics and diffusion models. The neural representations are then optimized using video score distillation sampling, which leverages motion priors from pretrained text-to-video diffusion models. Finally, the vector graphics are warped to match the representations resulting in smooth animation. Experimental results validate the effectiveness of our method in generating vivid and natural vector graphic animations, demonstrating significant improvement over existing techniques that suffer from limitations in flexibility and animation quality.

Related papers

• AniClipart: Clipart Animation with Text-to-Video Priors [28.76809141136148]
  We introduce AniClipart, a computational system that converts static animations into high-quality animations guided by text-to-video prior.<n>By differentiable As-Rigid-As-Possible (ARAP) shape deformation and differentiable rendering, AniClipart can be end-to-end optimized while maintaining rigidity.
  arXiv  Detail & Related papers  (2024-04-18T17:24:28Z)
• Dynamic Typography: Bringing Text to Life via Video Diffusion Prior [73.72522617586593]
  We present an automated text animation scheme, termed "Dynamic Typography" It deforms letters to convey semantic meaning and infuses them with vibrant movements based on user prompts. Our technique harnesses vector graphics representations and an end-to-end optimization-based framework.
  arXiv  Detail & Related papers  (2024-04-17T17:59:55Z)
• StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis [112.25071764647683]
  StrokeNUWA is a pioneering work exploring a better visual representation ''stroke tokens'' on vector graphics. equipped with stroke tokens, StrokeNUWA can significantly surpass traditional LLM-based and optimization-based methods. StrokeNUWA achieves up to a 94x speedup in inference over the speed of prior methods with an exceptional SVG code compression ratio of 6.9%.
  arXiv  Detail & Related papers  (2024-01-30T15:20:26Z)
• AnimateZero: Video Diffusion Models are Zero-Shot Image Animators [63.938509879469024]
  We propose AnimateZero to unveil the pre-trained text-to-video diffusion model, i.e., AnimateDiff. For appearance control, we borrow intermediate latents and their features from the text-to-image (T2I) generation. For temporal control, we replace the global temporal attention of the original T2V model with our proposed positional-corrected window attention.
  arXiv  Detail & Related papers  (2023-12-06T13:39:35Z)
• MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model [74.84435399451573]
  This paper studies the human image animation task, which aims to generate a video of a certain reference identity following a particular motion sequence. Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion. We introduce MagicAnimate, a diffusion-based framework that aims at enhancing temporal consistency, preserving reference image faithfully, and improving animation fidelity.
  arXiv  Detail & Related papers  (2023-11-27T18:32:31Z)
• AnimateAnything: Fine-Grained Open Domain Image Animation with Motion Guidance [13.416296247896042]
  We introduce an open domain image animation method that leverages the motion prior of video diffusion model. Our approach introduces targeted motion area guidance and motion strength guidance, enabling precise control of the movable area and its motion speed. We validate the effectiveness of our method through rigorous experiments on an open-domain dataset.
  arXiv  Detail & Related papers  (2023-11-21T03:47:54Z)
• DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors [63.43133768897087]
  We propose a method to convert open-domain images into animated videos. The key idea is to utilize the motion prior to text-to-video diffusion models by incorporating the image into the generative process as guidance. Our proposed method can produce visually convincing and more logical & natural motions, as well as higher conformity to the input image.
  arXiv  Detail & Related papers  (2023-10-18T14:42:16Z)
• Generative Image Dynamics [80.70729090482575]
  We present an approach to modeling an image-space prior on scene motion. Our prior is learned from a collection of motion trajectories extracted from real video sequences.
  arXiv  Detail & Related papers  (2023-09-14T17:54:01Z)
• Unsupervised Learning of Style-Aware Facial Animation from Real Acting Performances [3.95944314850151]
  We present a novel approach for text/speech-driven animation of a photo-realistic head model based on blend-shape geometry, dynamic textures, and neural rendering. Our animation method is based on a conditional CNN that transforms text or speech into a sequence of animation parameters. For realistic real-time rendering, we train a U-Net that refines pixelization-based renderings by computing improved colors and a foreground matte.
  arXiv  Detail & Related papers  (2023-06-16T17:58:04Z)
• Animating Through Warping: an Efficient Method for High-Quality Facial Expression Animation [11.522437263308984]
  We propose a novel framework known as Animating Through Warping (ATW) to enable efficient animation of HD images. It only requires the generator to be trained on small images and can do inference on an image of any size. Experiments show the effectiveness and efficiency of our method in generating high-resolution animations.
  arXiv  Detail & Related papers  (2020-08-01T23:52:33Z)
• Neural Human Video Rendering by Learning Dynamic Textures and Rendering-to-Video Translation [99.64565200170897]
  We propose a novel human video synthesis method by explicitly disentangling the learning of time-coherent fine-scale details from the embedding of the human in 2D screen space. We show several applications of our approach, such as human reenactment and novel view synthesis from monocular video, where we show significant improvement over the state of the art both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2020-01-14T18:06:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.